Hard Lessons From Running AI Agents in Production: MCP Failures, Env Leaks, and Infinite Loops

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A software engineer shared key lessons learned after deploying AI agents in production environments, where architectural oversights caused serious failures rather than model errors. A misconfigured Docker Compose file inadvertently exposed a production database connection string, which the LLM then included verbatim in its output. Model Context Protocol (MCP) rejections, often triggered by expired tokens, rate limits, or context overflow, were found to cascade silently unless handled as first-class recoverable events. The engineer also encountered non-terminating 70-line execution loops, highlighting the need for explicit failure boundaries in multi-step agent reasoning. Key remedies included scoped environment variables, dynamic credential injection, output sanitization, and dedicated MCP rejection handlers with exponential backoff logic.

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